Dynamic socialized collaboration nodes

ABSTRACT

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining ledger data of a blockchain ledger; examining node data of a plurality of candidate nodes, wherein the examining node data includes examining data of candidate nodal networks associated to respective ones of the plurality of candidate nodes; and transitioning blockchain ledger access in dependence on the examining of the ledger data and in dependence on the examining of the node data, wherein the transitioning blockchain ledger access includes transitioning blockchain ledger access between a first candidate node and a second candidate node of the plurality of candidate nodes.

BACKGROUND

Data structures have been employed for improving operation of computersystem. A data structure refers to an organization of data in a computerenvironment for improved computer system operation. Data structure typesinclude containers, lists, stacks, queues, tables and graphs. Datastructures have been employed for improved computer system operatione.g. in terms of algorithm efficiency, memory usage efficiency,maintainability, and reliability.

Artificial intelligence (AI) refers to intelligence exhibited bymachines. Artificial intelligence (AI) research includes search andmathematical optimization, neural networks and probability. Artificialintelligence (AI) solutions involve features derived from research in avariety of different science and technology disciplines ranging fromcomputer science, mathematics, psychology, linguistics, statistics, andneuroscience. Machine learning has been described as the field of studythat gives computers the ability to learn without being explicitlyprogrammed.

SUMMARY

Shortcomings of the prior art are overcome, and additional advantagesare provided, through the provision, in one aspect, of a method. Themethod can include, for example: examining ledger data of a blockchainledger; examining node data of a plurality of candidate nodes, whereinthe examining node data includes examining data of candidate nodalnetworks associated to respective ones of the plurality of candidatenodes; and transitioning blockchain ledger access in dependence on theexamining of the ledger data and in dependence on the examining of thenode data, wherein the transitioning blockchain ledger access includestransitioning blockchain ledger access between a first candidate nodeand a second candidate node of the plurality of candidate nodes.

In another aspect, a computer program product can be provided. Thecomputer program product can include a computer readable storage mediumreadable by one or more processing circuit and storing instructions forexecution by one or more processor for performing a method. The methodcan include, for example: examining ledger data of a blockchain ledger;examining node data of a plurality of candidate nodes, wherein theexamining node data includes examining data of candidate nodal networksassociated to respective ones of the plurality of candidate nodes; andtransitioning blockchain ledger access in dependence on the examining ofthe ledger data and in dependence on the examining of the node data,wherein the transitioning blockchain ledger access includestransitioning blockchain ledger access between a first candidate nodeand a second candidate node of the plurality of candidate nodes.

In a further aspect, a system can be provided. The system can include,for example a memory. In addition, the system can include one or moreprocessor in communication with the memory. Further, the system caninclude program instructions executable by the one or more processor viathe memory to perform a method. The method can include, for example:examining ledger data of a blockchain ledger; examining node data of aplurality of candidate nodes, wherein the examining node data includesexamining data of candidate nodal networks associated to respective onesof the plurality of candidate nodes; and transitioning blockchain ledgeraccess in dependence on the examining of the ledger data and independence on the examining of the node data, wherein the transitioningblockchain ledger access includes transitioning blockchain ledger accessbetween a first candidate node and a second candidate node of theplurality of candidate nodes.

Shortcomings of the prior art are overcome, and additional advantagesare provided, through the provision, in one aspect, of a method. Themethod can include, for example: examining ledger data of a blockchainledger; examining node data of a plurality of candidate nodes; andtransitioning blockchain ledger access in dependence on the examining ofthe ledger data and the node data, wherein the transitioning blockchainledger access includes transitioning blockchain ledger access to acertain node of the plurality of candidate nodes.

In another aspect, a computer program product can be provided. Thecomputer program product can include a computer readable storage mediumreadable by one or more processing circuit and storing instructions forexecution by one or more processor for performing a method. The methodcan include, for example: examining ledger data of a blockchain ledger;examining node data of a plurality of candidate nodes; and transitioningblockchain ledger access in dependence on the examining of the ledgerdata and the node data, wherein the transitioning blockchain ledgeraccess includes transitioning blockchain ledger access to a certain nodeof the plurality of candidate nodes.

In a further aspect, a system can be provided. The system can include,for example a memory. In addition, the system can include one or moreprocessor in communication with the memory. Further, the system caninclude program instructions executable by the one or more processor viathe memory to perform a method. The method can include, for example:examining ledger data of a blockchain ledger; examining node data of aplurality of candidate nodes; and transitioning blockchain ledger accessin dependence on the examining of the ledger data and the node data,wherein the transitioning blockchain ledger access includestransitioning blockchain ledger access to a certain node of theplurality of candidate nodes.

Additional features are realized through the techniques set forthherein. Other embodiments and aspects, including but not limited tomethods, computer program product and system, are described in detailherein and are considered a part of the claimed invention.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more aspects of the present invention are particularly pointedout and distinctly claimed as examples in the claims at the conclusionof the specification. The foregoing and other objects, features, andadvantages of the invention are apparent from the following detaileddescription taken in conjunction with the accompanying drawings inwhich:

FIG. 1 depicts a system having an authority, a blockchain ledger andcandidate nodes having access or prospective access in respect to theblockchain ledger according to one embodiment;

FIG. 2 is a flowchart illustrating a method for performance by anauthority according to one embodiment;

FIG. 3 is a flowchart illustrating a method for performance by anauthority interacting with a blockchain ledger and with candidate nodesaccording to one embodiment;

FIG. 4 depicts a system having additional nodes that can formreconfigured nodal networks with existing nodal networks according toone embodiment;

FIG. 5 depicts a system having prospective nodal networks according toone embodiment;

FIG. 6 depicts a system having prospective nodal networks according toone embodiment;

FIGS. 7-10 depict training and use of various predictive models bymachine learning according to various embodiments;

FIG. 11 depicts a computing node according to one embodiment;

FIG. 12 depicts a cloud computing environment according to oneembodiment; and

FIG. 13 depicts abstraction model layers according to one embodiment.

DETAILED DESCRIPTION

System 100 for managing transfer of access to a ledger is illustrated inFIG. 1. System 100 can include authority 110, having an associated datarepository 112, ledger 120 at “A” and nodal networks 131A-131Z.Authority 110, ledger 120 at “A”, nodal networks 131A-131Z can be incommunication with one another via network 180. System 100 can includenumerous devices as depicted in FIG. 1 which can be computing node baseddevices connected via network 180. Network 180 can be a physical networkand/or a virtual network. The physical network can be, for example, aphysical telecommunications network connecting numerous computing nodesor systems such as computer servers and computer clients. The virtualnetwork can, for example, combine numerous physical networks or partsthereof into a logical virtual network. In another example, numerousvirtual networks can be defined or a single physical network.

According to one embodiment, authority 110 can be external to ledger 120at “A” and to nodes of the various nodal networks 131A-131Z. Accordingto another embodiment, one or more of authority 110, ledger 120 at “A”,and candidate nodes NA-NZ of nodal networks 131A-131Z can be co-locatedwith one another. Ledger 120 at “A” can be a distributed ledger that canbe distributed to be associated to each of various candidate nodes NA-NZof respective nodal networks 131A-131Z where candidate nodes are membernodes of a blockchain network having access to ledger 120. Ledger 120 at“A” can be an instance of ledger 120 configured as a database that isassociated to authority 110.

According to one embodiment, ledger 120 can be an immutable ledger andcan be provided by a blockchain ledger. Ledger 120 can include a list ofrecords, called blocks, which can be linked together using cryptography.Each block of ledger 120 can include a cryptographic hash of a previousblock, a time-stamp, and transaction data, e.g., as can be representedby a Merkle Tree Root Hash. Ledger 120, which can be provided by ablockchain ledger, can be resistant to modification of data. Ledger 120can be configured so that once a block of data is recorded into ledger120, the data cannot be altered retroactively without alteration of allsubsequent blocks. According to one embodiment, alteration of ledger 120can be restricted and, according to one embodiment, can be permissible,e.g., only on consensus of a network majority. Each block of ablockchain can contain a hash (that is a digital fingerprint or uniqueidentifier), time-stamped batches of recent valid transactions and thehash of the previous block. Previous block hash can link the blockstogether and prevent any block from being altered or a block beinginserted between two existing blocks and, this way, each subsequentblock strengthens the verification of the previous block and, hence, theentire blockchain.

The method renders the blockchain tamper-evident, leading to theattribute of immutability. Ledger 120 can be a distributed ledger thatcan be distributed to candidate nodes. Candidate nodes of system 100 caninclude candidate nodes NA-NZ. Each candidate node can be included inand associated with a nodal network. Candidate node NA can be associatedwith nodal network 131A, candidate node NB can be associated to nodalnetwork 131B, and candidate node NC can be associated to nodal network131C. Further, candidate node NZ can be associated to nodal network131Z. Nodal networks 131A-131Z can be social networks according to oneembodiment. Ledger 120 can store various data. According to oneembodiment, ledger 120 can store work project data, such as work projectdata of an IT ticket order. Various stages of a project can be worked onby various member users who are associated to respective candidate nodesNA-NZ.

According to one embodiment, ledger 120 can include an original worklist. As member users work on a project, the members can add transactiondata, including documents to ledger which, on completion, become part ofthe ledger 120. Ledger 120 can be a distributed ledger and candidatenodes NA-NZ can be member nodes of a blockchain network having access toledger 120 or can be nodes that are qualified to become member nodes ofa blockchain network having access to ledger 120 on invitation.Authority 110 can mediate different access rights defined by permissionsfor different members. For performance of a handoff transition from afirst candidate node to a second node, authority 110 can relegate accessof the first candidate node and promote access of the second candidatenode.

System 100 can be configured so that when a member completes an updateto ledger 120, the updated ledger is distributed to all candidate nodesNA-NZ. System 100 can be configured so authorization for transactions tobe appended to ledger 120 is in dependence on a quorum of member nodesvalidating the transaction. Authority 110 together with member nodeshaving access to ledger 120 can define a blockchain network.

Each candidate node NA-NZ can participate in a nodal network. Candidatenode NA can participate in nodal network 131A, candidate node NB canparticipate in nodal network 131B, candidate node NC can participate innodal network 131C, and candidate node NZ can participate in nodalnetwork 131Z. Each nodal network can include social network nodes thathave a social connection to a candidate node NA-NZ. Candidate node NAcan include first order social contacts N1 and N2, second order contactsN3, N4 and N5, and third order contact N15. Candidate node NB caninclude first order contacts N11, N12 and N13, second order contacts N14and N15, and third order contact N3. Social contact nodes N3 and N5 canbe shared between nodal network 131A and 131B where, according to oneembodiment, nodal network association is limited to third order contactsof a candidate node. Candidate node NC can include first order contactsN21 and N22 and second order contact N23. Candidate node NZ can includerepresentative first order contact N. The various nodal networks caninclude edge connections as are depicted in FIG. 1. The edges betweenthe nodes of the various nodal networks 131A-131C can specify one ormore skill topic classification associated to connections of a socialrelationship.

In FIG. 1, skill topic classifications are specified as being either E1or E2 or both E1 and E2, “E1E2”. E1 can be a first skill topicclassification and E2 can be a second skill topic classification.Embodiments herein recognize that users can have social relationshipsthat are based upon a skill topic classification of the user to whomthey are connected. According to one embodiment, E1, as depicted in FIG.1, can indicate the skill topic classification “software developer”whereas the skill topic classification E2 can indicate theclassification “hardware architect”. The edge label “E1” can represent asocial relationship between first and second nodes based on first andsecond nodes having common work experience in the skill topicclassification “software development” whereas the edge label E2 canrepresent a social relationship between first and second nodes based onfirst and second nodes having common work experience in the skill topicclassification “hardware architect”. Some edges are labeled both E1 andE2 to indicate common work experience between first and second nodes inthe skill topic classifications “software development” “hardwarearchitect”.

According to one embodiment, authority 110 can be configured tointelligently hand off access to ledger 120. The intelligent handing offof access can include, e.g., handing off of an editing access right toledger 120. According to one example, authority 110 can intelligentlymanage the transfer of access to ledger 120 from candidate node NA, tocandidate node NB, and then to candidate node NC. Authority 110 can beconfigured to mediate access transitions according to any order betweencandidate nodes NA-NZ in dependence on one or more criterion.

According to one embodiment, ledger 120 stores data on a work project,and a user of a candidate node works on the work project with assistancefrom users of that candidate node user's associated nodal network. Forexample, for the performance of work item tasks by a user of candidatenode NA can engage the assistance of users of nodes within nodal network131A, such as node N1, node N2, node N3, node N4, and node N5 and nodeN15 (FIG. 1).

Authority 110 can transition access to ledger 120 between first andsecond nodes based on one or more criterion being satisfied. Authority110 transitioning access between first and second nodes can includeauthority 110 handing off access from the first node to the second node.When a certain candidate node, e.g. candidate node NA, has editingaccess to ledger 120, a user of certain candidate node NA can edit i.e.,add to blocks of ledger 120 as new tasks of a work project areperformed. Embodiments herein recognize that while it can be beneficialto hand off an access right such as an editing right to a ledger in avariety of scenarios, such as in completion of a work project,challenges exist. Embodiments herein recognize that as a work projectproceeds, users of nodes within a nodal network, such as nodal network131A may have diminished capacity to complete a work project. Forexample, as the skills preferred for completion of a work projectchange, embodiments herein recognize that the skills of users withinnodal network 131A, can become mismatched with respect to the workproject skill topic preferences. For example, a current nodal networkhaving primary access to ledger 120 through a certain candidate node canhave a high level of skill in skill classification A and a low level ofskill in skill classification B while the preferred skill topicclassification for a work project transitions from A to B. Also, throughthe passage of time, a current time can advance beyond the scheduledavailable work hours of users within nodal network 131A. Example highlevel scenarios which can trigger intelligent access transition betweena first nodal network 131A by candidate node NA to another nodal network131B by another candidate node NB, are now herein described.

According to one embodiment, a criterion for use in examining whetheraccess to a ledger 120 is to be transitioned can include a topiccriterion. In reference to the work project example embodiments hereinrecognize that a work project can include topics that are associated todifferent skill categories and that the topics of a work projectassociated to skill categories can change as a work project advances.For example, at an earlier stage of a work project, a first expert skillcan be designated preferred and in a later stage of a work project, asecond expert skill can be designated preferred. In an IT ticketexample, a software development skill topic classification can bereturned as preferred skill topic classification for a first stage and ahardware architecture skill topic classification can be returned as apreferred skill for a second stage.

Referring to FIG. 1, it can be seen that nodal network 131B can includeskill topic classification capacities that are differentiated from theskill topic classification capacities of nodal network 131A. Namely,nodal network 131B can have skill capacities in the E2 skill topicclassification that are greater than the E2 skill topic classificationsof nodal network 131A. In the described example, E2 can specify a“hardware architecture” skill topic classification capacities and E1 canspecify a “software development” skill topic classification capacities.Accordingly, it can be seen that in one high level example, authority110 based on a topic criterion can transition access to ledger 120 froma first candidate node to candidate node NB and its associated nodalnetwork 131B based on a determination that a skill topic of a workproject has transitioned from a skill topic associated to the skillidentified by the E1 identifier to a skill identified by the E2identifier.

Referring to nodal network 131C, nodal network 131C can have skillcapacities in both the E1 and E2 skill topic classifications that arereduced relative to those capacities in nodal network 131A and nodalnetwork 131B. Notwithstanding authority 110 can based on certain one ormore criterion handoff node access to ledger 120 to candidate node NC.

Authority 110 according to one embodiment can apply an availabilitycriterion in determining whether to handoff access to a new candidatenode. Embodiments herein recognize that users who perform work on workprojects can have limited availability which often can be dependent onthe hour of a day. Worker users can be available to work on projects,but often at preset time periods, e.g. 8 am-7 pm EST.

According to one embodiment, system 100 as set forth in FIG. 1 can beused to support a work project that originates on the east coast of theUnited States (U.S.) and which transitions to the west coast of the U.S.The user of candidate node NA is located on the east coast of the U.S.,the user of candidate node NB is also located on the east coast of theU.S., and the user of candidate node NC is located on the west coast ofthe U.S. The majority of users of nodes of nodal network 131A can belocated on the east coast, the majority of users of nodes of nodalnetwork 131B can be located on the east coast, and majority of users ofnodes of nodal network 131C can be located on the west coast.

The users of nodal network 131A and 131B in the described example cancomplete software development or hardware architecture skill topicclassified items of the work project from the times 8 am-7 pm EST.However, after 7 pm the users associated with the nodes of nodalnetworks 131A and 131B can become unavailable.

Authority 110 according to one embodiment applying an availabilitycriterion can examine calendar, e.g. work schedule information of usersof nodes within a nodal network and can return access transfer decisionbased on such examined data. In the described example, authority 110 cantransfer access to ledger 120 to candidate node NC, based on applicationof an availability criterion wherein there is a determination that thecurrent time is approaching 7 pm EST and that the users of nodes withinnodal network 131C are available to perform items of the work project,and that the users of nodes within nodal networks 131A and 131B areunavailable.

Authority 110 can be configured to facilitate the intelligent transitionof access to ledger 120 between first and second different nodes ofsystem 100, such as between candidate nodes NA and NB or betweencandidate nodes NA and NC or between candidate nodes NC and NB orbetween any other combination of first and second candidate nodes.

For providing the functionality herein, data repository 112 can includeledger attributes area 2121 for storing data on attributes of ledger120, node profile area 2122 for storing data such as skills data andavailability data for nodes of nodal networks 131A-131Z of system 100and prospective nodal networks that can be generated by system 100,schedule area 2123 for storing data on a current schedule for system 100which schedule can include, e.g. a schedule for transition of access toledger 120 among nodes such as candidate nodes NA-NZ. Data repository112 in results area 2124 can store historical data specifying results ofa nodal networks 130A-130Z performing tasks of a work subject. Datarepository 112 in members area 2125 can store data on members of ablockchain network having access to ledger 120, including e.g. data onthe time of membership initiation, historical data on times ofrelegations and promotions of blockchain membership and/or accessright(s) e.g. defined by permission(s), data specifying a schedule ofaccess rights including scheduled transitions of access rights betweennodes.

According to one embodiment, access rights of blockchain members whohave access to ledger 120 can change over time, e.g. in accordance withdata stored in schedule area 2123. An access right can change, e.g. froma “read only” access right to a “read and edit” access right. Accordingto one embodiment, authority 110 can dynamically adjust one or morepermission defining one or more access right to members of a blockchain.Authority 110 for example, can transition an access right of a candidatenode from a “read only” access right to a “read and edit” access rightwhen access handover is transitioned to the certain candidate node. Inperforming an access handoff, authority 110 can relegate access (e.g.removing one or more permission) of a first exiting node and promoteaccess (e.g. adding one or more permission) of a second entering node.

Authority 110 can run preparation and maintenance process 111 so thatdata of areas 2121-2124 of data repository 112 is iteratively updatedfor optimized processing by other processes run by authority 110.

Authority 110 can run ledger data examining process 113 for examiningdata of ledger 120. Ledger data of ledger 120 can include, e.g. anoriginal work list that specifies a list of items to be completed, e.g.in succession and ledger 120 can be modified over time, e.g. as accessis transitioned to new candidate nodes of system 100, and as suchcandidate nodes complete tasks to append to the ledger 120 additionalblocks. Authority 110 running ledger data examining process 113 candetermine skill topics associated to ledger 120 at a specific futurepoint in time. A skill topic can refer to a preferred skill to perform atask specified in ledger 120 at a particular point in time. In oneexample, the particular point in time can be the current time. However,in another example the particular point in time can be a future point intime, e.g. N minutes in advance of the current time, e.g. 60 minutes.The specific future point in time can be a future time from theperspective of a human user perceivably delayed by a delay time relativeto a current time.

Embodiments herein envision advantages to performing “warm handoff”between nodes of candidate nodes NA-NZ, so that transitioning nodes i.e.both exiting and entering nodes, have notification of a handovertransition prior to the time of transition. In such an embodiment,further with reference to the IT ticket example, nodes can send one ormore message to advise an underlying customer as to the transition andas to data points relevant to the transition.

Authority 110 for performance of ledger data examining process 113 canexamine ledger data so as to predict skill requirements for a workproject for a particular future point in time. For such predicting,authority 110 can for example examine data of a work list thatreferences items to be completed in succession and can identify skilltopics associated to items of a work project to be completed subsequentto the current time. For performing examining of data for predicting askill topic associated with a future time, authority 110 can query apredictive model that has been trained by machine learning and usingtraining data, wherein the training data includes supervised learningtraining data that associates past work items with past work items skilltopics with next item work item skill topics.

Authority 110 running ledger data examining process 113 can run naturallanguage processing (NLP) process 114. Authority 110 using NLP process114 can segment and derive topics associated with work subject itemsincluded on ledger 120, including items of an original work list, aswell as appended items that are specified in a succession of blocks ofledger 120.

Authority 110 can run NLP process 114 to process data for preparation ofrecords that are stored in data repository 112 and for other purposes.Authority 110 can run a Natural Language Processing (NLP) process 114for determining one or more NLP output parameter of a message. NLPprocess 114 can include one or more of a topic classification processthat determines topics of messages and output one or more topic NLPoutput parameter, a sentiment analysis process which determinessentiment parameter for a message, e.g. polar sentiment NLP outputparameters, “negative,” “positive,” and/or non-polar NLP outputsentiment parameters, e.g. “anger,” “disgust,” “fear,” “joy,” and/or“sadness” or other classification process for output of one or moreother NLP output parameters e.g. one of more “social tendency” NLPoutput parameter or one or more “writing style” NLP output parameter.

Authority 110 can run NLP process 114 to process data for preparation ofrecords that are stored in data repository 112 and for other purposes.Authority 110 can run a Natural Language Processing (NLP) process 113for determining one or more NLP output parameter of a message. NLPprocess 114 can include one or more of a topic classification processthat determines topics of messages and output one or more topic NLPoutput parameter, a sentiment analysis process which determinessentiment parameter for a message, e.g. polar sentiment NLP outputparameters, “negative,” “positive,” and/or non-polar NLP outputsentiment parameters, e.g. “anger,” “disgust,” “fear,” “joy,” and/or“sadness” or other classification process for output of one or moreother NLP output parameters e.g. one of more “social tendency” NLPoutput parameter or one or more “writing style” NLP output parameter.

By running of NLP process 114 authority 110 can perform a number ofprocesses including one or more of (a) topic classification and outputof one or more topic NLP output parameter for a received message (b)sentiment classification and output of one or more sentiment NLP outputparameter for a received message or (c) other NLP classifications andoutput of one or more other NLP output parameter for the receivedmessage.

Topic analysis for topic classification and output of NLP outputparameters can include topic segmentation to identify several topicswithin a message. Topic analysis can apply a variety of technologiese.g. one or more of Hidden Markov model (HMM), artificial chains,passage similarities using word co-occurrence, topic modeling, orclustering. Sentiment analysis for sentiment classification and outputof one or more sentiment NLP parameter can determine the attitude of aspeaker or a writer with respect to some topic or the overall contextualpolarity of a document. The attitude may be the author's judgment orevaluation, affective state (the emotional state of the author whenwriting), or the intended emotional communication (emotional effect theauthor wishes to have on the reader). In one embodiment sentimentanalysis can classify the polarity of a given text at the document,sentence, or feature/aspect level—whether the expressed opinion in adocument, a sentence or an entity feature/aspect is positive, negative,or neutral. Advanced sentiment classification can classify beyond apolarity of a given text. Advanced sentiment classification can classifyemotional states as sentiment classifications. Sentiment classificationscan include the classification of “anger,” “disgust,” “fear,” “joy,” and“sadness.”

By running of NLP process 114, authority 110 can perform a number ofprocesses including one or more of (a) topic classification and outputof one or more topic NLP output parameter for a received message (b)sentiment classification and output of one or more sentiment NLP outputparameter for a received message or (c) other NLP classifications andoutput of one or more other NLP output parameter for the receivedmessage.

Authority 110 running candidate node examining process 115 can examinedata of nodes associated to a plurality of candidate nodes, such ascandidate nodes NA-NZ that are candidates for handoff of access toledger 120. Authority 110 running candidate node examining process 115can examine data of nodal networks, e.g. nodal network 131A for thecandidate node NA. Data subject to examining by candidate node examiningprocess 115 can include skill attribute data and/or availabilityattribute data for a nodal network, e.g. nodal network 131A-131Z. Skillattribute data can include skill topic classification data that is independence on skill topic classification of edges within a nodalnetworks 131A-131Z. Availability data can include, e.g. calendar deriveddata that is in dependence on work hour availability of users within anodal network 131A-131Z. According to one embodiment candidate nodesNA-NZ can be nodes that are qualified to become members of a blockchainnetwork having access to ledger 120. According to one embodimentcandidate nodes NA-NZ can be existing member nodes of a blockchainnetwork having access to ledger 120 of differing levels which differentlevels can be changed by an access handoff mediated by authority 110.Authority 110 running candidate node examining process 115 to examinedata of nodal networks can examine data of established and existingnodal networks as well as prospective nodal networks, which prospectivenodal networks can be generated in response to one or more outputprovided by authority 110.

Authority 110 running decision process 116 can apply artificialintelligence (AI) to return a decision to handoff access to ledger 120to a subsequent node, such as to a subsequent candidate node ofcandidate nodes NA-NZ. Authority 110 running decision process 116 caninclude authority using data returned by performance of ledger dataexamining process 113 and candidate node examining process 115.

Authority 110 running decision process 116 can employ a weight based AIformula that is in dependence on a skill topic classification factor andan availability factor. A skill topic classification of the skill factorcan be in dependence on an output of ledger data examining process 113so that the skill topic classification factor can match a skill topicclassification returned by examination of ledger data. Authority 110mediating a handoff of access to ledger 120 from a first node ofcandidate nodes NA-NZ to a second node of candidate nodes NA-NZ caninclude authority 110 relegating access of the first candidate node andpromoting access the second candidate node. A relegation of access caninclude e.g. terminating membership of the first node in a blockchainnetwork, and/or maintaining membership of the first node and terminatingan editing right of the first node in ledger 120. A promotion of accesscan include e.g. inviting the second candidate node to become a memberof a blockchain network in which members have access to ledger 120and/or adding one or more access right defined by one or more permissionto the second node having an associated user.

On completion transition of access to ledger 120 between a first exitingcandidate node and a second entering candidate node, the secondcandidate node can be regarded as a primary node having primary accessto ledger 120 and the nodal network associated to a primary node anengaged nodal network, e.g. which can be engaged to perform items ofwork project having transactions recorded in ledger 120.

Authority 110 can run machine learning process 117 for AI decisionoutputs that are of improved accuracy and reduced computationaloverhead. Authority 110 running machine learning process 117 can, forexample, train predictive models, e.g. so that ledger data examiningprocess 113 is able to better predict future skill topic classificationsassociated to a current project at a specific future point in time independence on skill topic classifications of work currently beingperformed, and/or which has been previously performed. Authority 110running machine learning process 117 can for example, train predictivemodels with use of supervised training, wherein training data includesattribute data associated with a nodal network 131A, aligned to a taskof a work project in connection with results data associated to suchengagements.

Authority 110 running membership process 118 can for example invite newnodes to become members of a blockchain network and can also terminatemembership of nodes based on one or more criterion being satisfied.Authority 110 running membership process 118 can also, e.g. for variouscandidate nodes NA-NZ dynamically adjust access to ledger 120 defined bypermissions. Permissions can include, e.g. “read only” access to ledger120, and as another example “read and edit” access to ledger 120.

Authority 110 can be configured according to one embodiment so thatmembership process 118 dynamically adjusts permissions associated tocandidate nodes so that only one or more candidate node of candidatenodes NA-NZ is given “editing” access to ledger 120 at a given time tothereby provide such advantages as avoiding multiple nodes working on awork project specified in ledger 120 simultaneously. An editing accessright can be regarded as a primary access right.

FIG. 2 is a flowchart illustrating a method 200 that can be performed byauthority 110. At block 210 authority 110 can run preparation andmaintenance process 111, e.g. to populate, prepare, and/or maintainvarious data of data repository 112 such as data of areas 2121-2124. Forperformance of preparation and maintenance process 111 authority 110 canbe configured to automatically process data inputs received from nodesof system 100 such as nodes defining nodal networks of prospective nodalnetworks. Authority 110 can run preparation and maintenance process 111iteratively until preparation and maintenance process 111 is terminatedat block 212. At block 220, authority 110 can run candidate nodeexamining process 115. A plurality of instances of examining process 115can be simultaneously run. Authority 110 can run examining process 115until examining process 115 is terminated at block 222. With theperforming of examining process 115 iteratively, authority 110 can berunning associated processes iteratively such as processes 113-114 andprocesses 116-118.

The flowchart of FIG. 3 illustrates authority 110 performing a method byway of authority 110 interacting with ledger 120 and nodes of nodesNA-NZ for performance of a method.

At block 2201 a candidate node of candidate nodes NA-NZ currently havinga primary access right to ledger 120 can be performing a task inrelation to a work project specified in ledger 120 and ledger 120 basedon data of the performed task can be updating ledger 120 for theappending of additional one or more blocks onto a blockchain of ledger120. Member nodes of a blockchain network having access to ledger canapprove by a quorum all transactions resulting in blocks being added toledger 120.

In response to received data received at block 1201, ledger 120 at block1202 can iteratively send project data to authority 110 and in responseto the project data received by authority 110 at block 1101, authority110 can perform examining of ledger data. Authority 110 can performexamining of ledger data at block 1101 and at block 1102 authority 110can send data returned by the examining of ledger data to datarepository 112 for receipt and storage by data repository 112 at block1121 into ledger attributes area 2121 of data repository 112. Examiningof ledger data can include according to one embodiment determining askill topic classification for a current work project. A returned skilltopic classification can include e.g. a returned current preferred skilltopic classification, and or a predicted future preferred skill topicclassification. As set forth in Table A, authority 110 at block 1101 cantag work data for skill topic classification.

TABLE A Text Label Set fabbione: ahoy Developer Training my fast i386 isstill running unstable Developer Training re Developer Training mdz: anynews? Developer Training mdz:permission to upload new isb with AMD64support? #1354, patch at Developer Training [WEBSITE LINK] phwoarDeveloper Training hey Developer Training pitti: Subject: hal 0.2.98released Developer Training jdub: nobody replied to my mail, butseriously, I want a NEEDINFO state :) Developer Training jdub: [WEBSITELINK] Developer Training mdz: i386 or amd64? Developer Training daniels:oh btw.. i think i have the fix for Xv on nvidia Developer Trainingjdub: [WEBSITE LINK] Developer Training jdub: permission for #1286?Developer Training fabbione: oh, what kind of drivers I'm using?Architect Training Mithrandir: it will cost me less to buy him an adsl;) Architect Training right, hopefully this is the last firefox buildArchitect Training can someone using ppc rungnome-audio-profiles-properties for me? Architect Training jdub: #1607OK? Architect Training Mark wants several questions moved back topre-reboot, and I really don't Architect Training want to duplicate thecode if I can help it Kamion: which mail are you talking about?Architect Training Kamion: you'll be pleased to know that john is nowbothering bruce Architect Training seb128: [WEBSITE LINK] ArchitectTraining morning Architect Training looks like my AP didn't survive thepower mess yesterday either. :-( Architect Training

As shown in Table A, work project data according to one example caninclude a blog specifying items of current work tasks of a current workproject having data of work items recorded in ledger 120. As shown inTable A, each row of data of the blog can be tagged with an applicableskill attribute classification e.g. in reference to Table A, “developer”for the skill topic classification “software development” or “architect”for the skill topic classification “hardware architect”. Machinelearning processes can be used for the return of the skill topicclassifications as will be set forth in further detail herein. Variousrules based criterion can be used for the return of a skill topicclassification of a current work project. According to one example, thecurrent skill topic classification for a work project to be taken as thelast entry of file data such as shown in Table A specifying currentwork. In another example, a weighted average can be used, e.g., thereturned work topic classification can be taken as the majorityclassification over a most recent predetermined period of time. Forexample, whereas “developer” is specified as the last classification butarchitecture is the classification in 60% of flagged data items over thelast one hour period of time. “hardware architect” can be returned asthe skill topic classification and not “software developer”. Accordingto one embodiment, authority 110 can be configured to predict a skillclassification of a skill preferred for performance of a work project ata time period in advance of the current time, e.g., 10 minutes inadvance, 20 minutes in advance, an hour in advance and so on.

Embodiments herein recognize that it can be advantageous to provide awarm transfer of candidate node access to ledger 120, e.g., so that anunderlying customer can be notified as to the transition and further sothat an exiting node subject to relegation of access and an enteringnode subject to promotion of access can share data regarding thetransition to improve the performance of work items of a work project.Authority 110 can perform various processes to facilitate such a warmtransfer.

According to one embodiment, authority 110 at block 1101 can examineledger data at a root block of a blockchain defining ledger 120. Such aroot block can define a master worklist for a work project, e.g., as canbe defined by a master services agreement. Authority 110 can examineboth work items of the master work list that are specified and cancompare the same to work items that are referenced in ledger 120 asbeing already completed. For returning a prediction of a preferred skilltopic classification for a specific future point in time, authority 110can examine data of such master work list to data specifying itemsalready completed to determine a next N items of a work project to beperformed e.g. in an order specified in a master worklist of ledger 120.

For example, if a master worklist lists four items with the fourth itembeing processed to return a skill topic classification of “hardwarearchitect” and according to ledger 120 the first three items havealready been performed, authority 110 based on the worklist dataincluded in the ledger data and the remaining ledger data whichindicates items already complete, can return prediction data specifyingthat a skill topic classification of the work project at a specificfuture point in time has the skill topic classification of “hardwarearchitect” (the next item listed in a master worklist which has not yetbeen performed).

Authority 110 performing examining at block 1101 to return a predictionas to skill topic classification of a work project at a future point intime can in addition, or alternatively, include authority 110 using apredictive model trained by machine learning processes for return ofprediction data. Machine learning processes for return of predictiondata specifying predicted future skill topic classification for a workproject using machine learning will be detailed further herein. Trainingdata for such a predictive model can include iterations of observedskill topic classifications for various time segments.

At block 2202, candidate nodes NA-NZ which can be candidate nodes thatmay be selected by authority 110 for handoff of access to ledger 120 canbe iteratively sending data for receipt by authority 110 at block 1103.In response to receipt of node data at block 1103 authority 110 canexamine node data and send resulting node attribute data to datarepository 112 for receipt and storage by data repository 112 at block1122. Data repository 112 receiving and storing node attribute data atblock 1122 can include data repository 112 storing data into nodeprofiles area 2122 of data repository 112. Node data sent at block 2202can include data for use by authority 110 in generating node attributedata. Authority 110 can receive node data at block 1103 based onreturned node attribute data can send returned attribute data forsending by authority 110 at block 1104 into data repository 112 forreceipt and storage by data repository 112 at block 1122 into nodeprofiles area 2122 of data repository 112. Returned node attribute datareturned at block 1103 can include node skill topic classifications andnode availability classifications for the nodal networks 131A-131Zassociated to each respective candidate node NA-NZ.

Authority 110 at examining node data block 1103 can activate candidatenode examining process 115 to examine nodes and edges of nodal network131A associated to candidate node NA and nodal network data for allremaining candidate nodes. Authority 110, in the described example asset forth in reference to FIG. 1 can return attribute scores for theskill topic classification E1 and the skill topic classification E2 foreach candidate nodal network 131A-131Z associated to a respectivecandidate node NA-NZ which can be selected as a succeeding nodal networkengaged to work on the work project referenced in ledger 120. Authority110 according to one embodiment can count the edges for eachclassification E1-E2 and can add weights to the classifications based onedge proximity to candidate node NA which serves as the root node fornodal network 131A. Edges closer to a root node can be weighted higherthan nodes spaced from a root node.

For return of node attribute data associated to node NA, authority 110can examine node data of nodal network 131A associated to node NA.Authority 110 can examine calendar data of the users of nodes N1-N5 andN15 for determining availability of the various users within nodalnetwork 131A. Authority 110 for each candidate nodal network 131A-131Zrespectively associated to a candidate node NA-NZ can generateavailability attribute data on a per skill topic classification basis sothat for each skill topic classification there can be recorded adifferent availability attribute data item.

Authority 110 at block 1103 can be returning node attribute data eachnodal network 131A-131Z associated to each candidate node NA-NZ ofsystem 100 and the attribute data can be subsequently stored by datarepository 112 at block 122 e.g. into node profiles area 2122 of datarepository 112.

Authority 110 at block 1105 can perform deciding as to a node forhandoff of access to ledger 120. The deciding at block 1105 can be independence on the examining of ledger data at block 1101 and theexamining of node data at block 1103.

At deciding block 1105, authority 110 can decide to relegate ledgeraccess of a first candidate node and to promote ledger access a secondcandidate node. The relegation can include removing a permission, e.g.an editing permission from the set of permissions including a readingright and an editing right. The promotion can include adding one or morepermission to the second candidate node, e.g. adding a ledger editingright to an existing read right.

Authority 110 performing deciding at deciding block 1105 can includeauthority 110 activating decision process 116 (FIG. 1). Authority 110running decision process 116 can include authority using data returnedby performance of ledger data examining process 113 and candidate nodeexamining process 115. Authority 110 performing deciding at block 1105can include authority 110 returning a predicted performance score foreach candidate node NA-NZ as set forth in Eq. 1 below.P=F ₁ W ₁ +F ₂ W ₂  (Eq. 1)Where P is a predicted performance score for a nodal network associatedto a candidate node, F₁ is a first factor, F₂ is a second factor and W₁and W₂, respectively, are weights associated with the first and secondfactors. In the described example, the factor F₁ can be a skill topicclassification factor for the returned work project classificationreturned by the examining of ledger data at block 1101. That is, wherethe examining of ledger data indicates the skill topic classification ofEq. 1. Factor F₁ is based on the skill topic classification attributepertaining to the skill topic classification Eq. 1. Still referring toEq. 1, the factor F₂ can be an availability factor returned for thecandidate node for the skill topic classification that matches thereturn skill topic classification returned by the examining of ledgerdata at block 1101.

Eq. 1 can be configured to have a weight W2 associated to factor F₂referencing availability that is sufficiently high so that accesshandoff to a certain candidate node is safely avoided unless there issufficient availability associated to a candidate node. In response toperforming deciding at block 1105 authority 110 can proceed to block1106 to perform sending a return scheduled data specifying a schedulefor handoff of access to ledger 120 between nodes. Scheduling data canbe received by data repository 112 at block 1123 for storage in datarepository 112 at block 1123.

Authority 110 performing examining node data at block 1103 can includeauthority 110 examining data of additional nodes, such as additionalnodes AN1, AN2, AN3 . . . ANZ as depicted in FIG. 4. Authority 110activating candidate node examining process 115 can include authority110 examining prospective nodal networks that are prospectivereconfigurations of existing nodal networks 131-131Z (FIG. 1) whereinprospective nodal network can be provided by the addition of one or moreadditional node AN1-ANZ therein. Referring to FIG. 5, for example,prospective nodal network 131A′ refers to nodal network 130A (FIG. 1) asmodified by the inclusion of additional nodes AN2 and AN3 which,according to FIG. 4, are in social network association prior to theirinclusion in nodal network 131A. Further referring to FIG. 5, nodalnetwork 131B′ refers to nodal network 131B (FIG. 1) as modified andreconfigured by the inclusion of additional nodal network AN1 therein.Further referring to FIG. 5, nodal network 131C′ refers to nodal network131C (FIG. 1) as modified by the inclusion of additional node ANZ.Authority 110 at block 1103 can include authority 110 examining withexisting nodal networks 131A to 131Z as shown in FIG. 1. Prospectivenodal networks, such as prospective nodal networks 131A′, 131B′ andnodal network 131C′, which can be prospectively provided as shown inFIG. 5.

Referring to FIG. 4, additional nodes AN1-ANZ can be nodes that are notcurrently connected to social network 131A-131Z, having a candidate nodesuch as candidate nodes NA-NZ where a candidate node is a candidate nodefor accepting a bandoff in a ledger transition. Alternatively, one ormore of additional nodes AN1-ANZ can be included in an existing nodalnetwork 131A-131Z having a candidate node NA-NZ. Embodiments hereinrecognize that social networks, such as collaborative social networksuseful for performance of tasks of a collaborative work projectspecified in a blockchain ledger, need not be static but rather can bedynamic social networks that are continually changing and, in someembodiments, changing in dependence on one more action of authority 110.According to one embodiment, authority 110 can be configured so thatauthority 110 provides one or more output to dynamically change anexisting nodal network 131A-131Z in a manner for improving theperformance of the nodal network in relation to a work project of ledger120.

According to one embodiment, system 100 can be configured so thatauthority 110 can provide one or more output to initiate sending ofinvites from one or more node of an existing social network 131A-131Z toone or more additional node AN1-ANZ a reconfiguration of an existingnodal network 131A-131Z. Embodiments herein recognize that nodal networkreconfigurations initiated by authority 110 are not limited toreconfigurations of the type described in reference to FIG. 5, but canalso include reconfigurations involving current one or more nodes of anexisting nodal network. FIG. 6 illustrates a reconfiguration of nodalnetwork 131A (FIG. 1). Referring to FIG. 6, nodal network 131A can bereconfigured to establish reconfigured nodal network 131A″ by thereordering of node N4 so that node N4 is directly connected as a firstorder node to node NA. Authority 110 can initiate the reconfigurationdepicted in FIG. 6 by processes that include providing an output toinitiate a sending of an invite by node NA to invite node N4 to become afirst order connection of node NA. Further in reference to FIG. 6,authority 110 can provide an output so that node N1 invites node ANZ tobecome a first order connection of node N1. Authority 110 can initiatereconfigurations of existing nodal networks by a process that includesproviding one or more output to initiate an invite by one or moreexisting node to another node, e.g., node N4, or node ANZ in the exampledescribed in connection with FIG. 6 to become a connection such as afirst order connection.

According to one embodiment, nodes of system 100 such as candidate nodesNA-NC, nodes N1-N, and nodes AN1-ANZ can subscribe to a service providedby authority 110 wherein authority 110 can provide an output to initiateautomatic sending of a social network connection invite from thesubscribing node. Thus, in reference again to FIG. 5, node NA can havesubscribed to a service provided by authority 110 permitting authority110 to provide an output to initiate the sending of a social networkconnection invite by node NA to additional node AN2, which invite can beaccepted by node AN2 so that the social network, including nodes AN2 andAN3, can become part of reconfigured nodal network 130A′ as depicted inFIG. 5.

Authority 110 performing deciding at block 1105 can include authority110 activating decision process 116 (FIG. 1). Authority 110 runningdecision process 116 can include authority 110 using data returned byperformance of ledger data examining process 113 and candidate nodeexamining process 115. Authority 110 for performing deciding at block1105 wherein node data includes node data of prospectively reconfigurednodal networks can include authority 110 applying Eq. 2 as set forthherein below.P=F ₁ W ₁ +F ₂ W ₂ +F ₃ W ₃  (2)Where P is a predicted performance score for a nodal network associatedto a candidate node; F₁ is a first factor; F₂ is a second factor; F₃ isa third factor; and W₁-W₃, respectively, are weights associated with thefirst through third factors. In the described example of Eq. 2, thefactors F₁ and F₂ and weights W₁ and W₂ can be as described inconnection with Eq. 1. F₃, on the other hand, can be a probabilityfactor that specifies a probability of a prospective nodal network beingestablished. For determining the probability of a prospective, e.g.,reconfigured nodal network being established, authority 110 can examinedata of a predictive model that predicts the likelihood of an invitednode of system 100 accepting an invitation for connection to a socialnetwork which connection will, if formed, result in reconfiguration of anodal network such as one of nodal networks 131A-131Z as set forth inFIG. 1 and FIG. 4. Such predictive model can be trained using machinelearning as set forth herein.

Authority 110 at deciding block 1106 can return a decision specifying acandidate node to which to transition ledger access. Authority 110 atdeciding block 1106 can return a decision specifying a candidate node towhich to transition ledger access on the basis of which candidate nodalnetwork associated to a candidate node produced the highest predictedperformance score, P. According to one embodiment, candidate nodalnetworks can be limited to existing nodal networks having a candidatenode NA-NZ. According to one embodiment, candidate nodal networkssubject to performance predicting can include prospective nodal networkshaving a candidate node NA-NZ which prospective nodal networks can begenerated in dependence on one or more output provided by authority 110.Authority 110 can provide permutations of prospective nodal networks andcan subject such prospective nodal networks and existing nodal networksto performance predicting using Eq. 2. On the return of an actiondecision at block 1106 to hand off access of ledger 120 to a candidatenode of a prospective nodal network, authority 110 can provide one ormore output to generate the prospective nodal network to establish theprospective nodal network as an established and existing nodal network.

Embodiments herein recognize that scoring of candidate nodal networksusing Eq. 1 or Eq. 2 can be in dependence on a matching between areturned skill topic classification of examined ledger data, and areturned skill topic attributed of examined node data. For example,Factors F1 and F2 can be factors in dependence on a matching between aledger skill topic classification and a nodal network skill topicclassification. Accordingly, authority 110 can return a selectedentering candidate node to which to transition access to ledger 120 independence on a topic matching between ledger 120 and a nodal network(e.g. existing or prospective) of system 100.

At block 1107, authority 110 can provide one or more output. One or moreoutput at block 1107 can include one or more communication to candidatenodes that are parties to a handoff. Authority at block 1107 can send toan exiting node and an entering node schedule data and control data. Theschedule data can include data that specifies a time for a transitionhandoff at which time an exiting node's access can be relegated andfurther at which time an entering node's access can be promoted, e.g.,to include an editing right to ledger 120 in addition to a read right.

According to one embodiment, to facilitate a warm transfer, authority110 at block 1107 can send schedule and control data a preset time priorto a transition time. The preset time can be, e.g., 10 minutes, ahalf-hour or an hour from the time of receipt by an exiting node and anentering node at block 2203. The receipt by an exiting and entering ofcandidate nodes NA-NZ at block 2203 of scheduling data prior to atransition time handoff facilitates the users of the exiting andentering nodes communicating with one another via the entering andexiting and entering nodes data regarding the transition and allows theusers of the exiting and entering nodes to communicate with anunderlying customer regarding the transition.

The control data can be control data to facilitate access relegation ofan exiting candidate node and/or access promotion of an enteringcandidate node. The control data can include control data forfacilitation of relegation of permissions in the case of an exiting nodeor control data for facilitation of promotion of permissions in the caseof an entering node.

In the case a prospective nodal network is selected as a succeedingengaged nodal network associated to a selected entering candidate node,authority 110 providing one or more output at block 1107 can includeauthority 110 providing one or more output to generate the prospectivenodal network so that an established and existing nodal network isgenerated in accordance with the selected prospective nodal network. Theone or more output provided by authority 110 can include one or moreoutput to initiate the sending by one or more certain node of a nodalnetwork associated to a candidate node (which can include the candidatenode) to automatically invite one or more other node to connect to theone or more certain node.

Authority 110 at block 1105 can return a decision that a succeedingengaged nodal network is a yet un-established prospective nodal network,but the selected prospective nodal network can have an associatedcandidate node in common with a candidate node currently functioning asa primary node. In such a scenario one or more output provided at block1107 can include one or more output to generate an established nodalnetwork (e.g. to initiate one or more connection invite) but can beabsent of one or more output to transition the current primary node.

At block 1108, authority 110 can perform examining of a performance of amost recently engaged nodal network in the performance of tasks of acollaborative work project. For performing such examining, authority 110can count a number of tasks that have been successfully completed duringthe most recent engagement. For performing such examining, authority 110can count a percentage of tasks that have been successfully completedduring the most recent engagement out of tasks that have been attempted.The value of a performance score parameter can be returned based, e.g.,on the rate of task performance, e.g., tasks divided by the time periodof the engagement and can be weighted by the difficulty of the tasks.

At block 1109, authority 110 can perform machine learning training totraining various predictive models by methods that involve use ofmachine learning. Authority 110 can return a predicted performance scoreusing Eq. 1 for each candidate node NA-NZ and can return the candidatenode NA-NZ having the highest predicted performance score as the targetentering node of a handover transition.

Authority 110 can be provided by one or more computing nodes. It can beregarded as a candidate node of a blockchain network that has access toledger 120, a copy of which can be stored in a database at A associatedto authority 110.

Authority 110 at block 1109 can perform machine learning training ofvarious predictive models that can be used by authority 110 in theperformance of various processing performed by authority 110. Oncompletion of block 1109 authority 110 at block 1110 can return to block1101.

Authority 110 can use predictive model 4004 as depicted in FIG. 7 forreturn of skill topic classifier labels associated with text of anongoing work project. Predictive model 4004 can be trained with use ofmachine learning process 117. Predictive model 4004 can be subject totraining with use of training data as depicted in FIG. 7 which isiteratively input into predictive model 4004 for training. Training datacan include segments of text from a work project file, e.g. a blog asshown in Table A, accompanied by administrator defined skill topicclassifier data associated with the text. For example, with reference toTable A, work project data such as the blog as shown in Table A, can beiteratively generated throughout the course of deployment of system 100.An initial set of text based blog data can be manually tagged byadministrator action, e.g. with use of an administrator user interface.The administrator tagged data can be applied as training data to thepredictive model 4004. Segments of text can be applied as training datain association with administrator defined text skill topic classifiersfor the text segments. Table B depicts code for use in trainingpredictive model 4004 for use in return of skill topic classifiers forentered text.

TABLE B # Support Vector Machine loss_fun=‘huber’ classifier_svm_none =Pipeline([(‘vect’, CountVectorizer( )), (‘tfidf’, TfidfTransformer( )),(‘clf’, SGDClassifier(loss=loss_fun, penalty=‘none’, alpha=1e−3,n_iter=5, random_state=42)), ]) _ = classifier_svm_none.fit(X_train,y_train)

After an initial training of predictive model 4004, predictive model4004 can be configured to be responsive to query data. The query datacan include unlabeled segments of text. The query data can be presentedto predictive model 4004 and once trained, predictive model 4004 canreturn an output. The output being an automated skill topic classifierlabel applied to the text data. Early on in the deployment of system 100the skill topic classifier labels depicted in Table A can be manuallyapplied, but after training performed by machine learning of predictivemodel 4004, predictive model 4004 can automatically generate suchlabels.

Over the course of deployment of system 100, an administrator user cancontinue to refine the performance of predictive model 4004. Forexample, a displayed user interface for use by an administrator user caninclude controls that allow a user to manually label new terms, whichthe administrator user believes, e.g. may be previously unencounteredand difficult for predictive model 4004 to generate an accurateprediction of a skill topic classifier. Thus, in spot instances anadministrator user is able to manually label select terms, e.g. textterms of a blog at the option of the administrator user. After aninitial main training session, e.g. at the onset of deployment of system100 predictive model 4004 can be continued to be subject to trainingdata in spot instances. Using training data derived in the describedsituation where an administrator user optionally defines manuallydefined skill topic classifier labels for certain terminology, e.g.terms that an administrator user regards as terms newly introduced tosystem 100.

FIG. 8 depicts a predictive model 4008 for use in returning predictionsof future skill topic classifiers associated to an ongoing work project.As set forth in reference to block 1101, authority 110 can predict askill topic classifier for a work project at a certain future time, e.g.a preset future time, rather than return a skill topic classifier for acurrent time. Embodiments herein recognize that it can be advantageousfor authority 110 to manage a “warm transfer”.

Predictive model 4008 as depicted in FIG. 8 can facilitate return of apredicted future skill topic classifier that specifies a predictedfuture skill topic classifier at a certain future time that is a time inthe future in relation to a current time. Predictive model 4008 canreturn a predicted skill topic classifier for time T=T₂ where T₂ issubsequent to time T=T₁. For training of predictive model 4008,authority 110 can perform machine learning by application of trainingdata to predictive model 4008. The applied training data applied topredictive model 4008 can include training data that specifies arelationship between classifications during certain sequences of time inrelation to observed skill topic classifications subsequent to thesequence of end time segments.

By application of the training data predictive model 4008 can learntrends of skill topic classifications that are in dependence on priorskill topic classifications. Predictive model 4008 is able to learn atrend wherein for example, a pattern wherein a second skill topicclassification is expected to follow a sequence of first and secondskill topic classifications according to a certain timing pattern.

Training data applied to predictive model 4008 can include skill topicclassifications for N successive time segments and observed skill topicclassification for time segment N+1. With the presence of the observedskill topic classification and the training data, predictive model 4008defines a predictive model that is trained by supervised learning.Predictive model 4008, once trained, can be responsive to query data.The query data can include skill topic classifications for N most recenttime segments relative to the current time T=T₁. Based on the trainingof predictive model 4008, predictive model 4008 is able to return outputdata in response to the received query data. The output data can includea predicted skill topic classifier for time T=T₂ where T₂ is subsequentto the current time T=T₁. T₂ can be subsequent to T₁ by a certain delayperiod, e.g. a preset delay period that is selected by an administratoruser. The delay period can be selected so that the predicted skill topicclassifier is associated to a time subsequent to the scheduledtransition time that can be scheduled at deciding block 1105. Thus, aselected targeted entering node to which an access right can be handedoff can have skill attributes matched the predicted skill topicclassifier for the examined ledger 120 for a specific future point intime. Authority 110 can train and use J instances of predictive model4008. For example, authority 110 can instantiate an instance ofpredictive model 4008 for each of J classifications of collaborativework projects being facilitated by authority 110.

FIG. 9 depicts predictive model 4012 that can be used by authority 110to return a refined prediction of performance of a candidate nodes NA-NZbeing examined for identification as a target node in a transitionhandoff facilitated by authority 110. Authority 110 as explained withreference to Eq. 1 can return a scoring parameter value that specifies apredicted performance of a candidate node NA-NZ if selected as atargeted entering node in a transition handoff. Further as explained inreference to block 1108, authority 110 can perform examining of engagedcandidate nodes and their associated nodal networks, e.g. nodal networks131A-131Z to return performance data specifying performance of thecandidate node and its associated nodal network during the engagement.

Predictive model 4012 can be trained by machine learning to return anadjusted predicted parameter value that improves the return data of Eq.2. Authority 110 can apply as training data to predictive model 4012predicted performance data that is generated using Eq. 1 in combinationwith observed performance data, e.g. the performance data returned bythe performance of performance examining block 1108. On being trained,predictive model 4012 is able to respond to query data.

The query data can include a predicted performance value returned byapplication of Eq. 1 herein and the returned output data returned byapplication of the query data can include an adjusted predictedperformance value. Accordingly, an output of predictive model 4012 canbe adjusted based on whether the applicable candidate node tends tooverperform or underperform relative to its performance generated usingEq. 1. Authority 110 can instantiate K instances of predictive model4012. For example, authority 110 can instantiate an instance ofpredictive model 4012 for each of K candidate nodes NA-NZ of system 100,each candidate node having an associated nodal network 131A-131Z.

In FIG. 10 there is depicted a predictive model 4016 trained by machinelearning for use in predicting a likelihood of a node accepting aninvitation to join a social network from a certain inviter node. Forexample, as explained in reference FIG. 5, inviter node NA can inviteadditional node AN2 (FIG. 4) to socially connect to node NA and as aresult of an acceptance by node AN2, the social network comprising nodesAN2 and AN3 (FIG. 4) can become part of prospective nodal network 130A′(FIG. 5) which prospective nodal network 130A′ (FIG. 5) can become anestablished existing nodal network on the acceptance of node AN2 to thedescribed invitation by node NA. Referring to predictive model 4016,predictive model 4016 can be trained using training data which trainingdata can include inviter profile data and accept reject data associatedwith prior instances of invites to a node. All instances of predictivemodel 4016 can be provided, e.g., once instance for each node of system100. Differently trained predictive models can be provided for eachdifferent node so that the set of trained predictive models 4016accurately reflect differences in behavior among different users of thedifferent nodes of system 100. Authority 110 can iteratively applytraining data to predictive model 4016. For example, authority 110 caniteratively apply training data each time there is an invitation toconnect to a node corresponding to the instance of predictive model 4016and for each invite instance training data can also include the acceptreject data associated with the invite instance specifying whether theinvitation was accepted or rejected. Predictive model 4016 once trainedcan be configured to respond to query data. The query data can include atest invite provided by authority 110. For example, in determiningfactor F3 during the course of applying Eq. 2 as set forth herein.Authority 110 can apply test data to a predictive model for a nodesubject to a data examination to determine a probability that an inviteto connect will be accepted by the node to thereby reconfigure a nodalnetwork. In response to application of test invite data to predictivemodel 4016, predictive model 4016 can return acceptance data, e.g., inthe form of a value of between 0.0 and 1.0 which indicates a confidencelevel that an invite will be accepted. The test invite data can includea profile of a prospective inviter node forming part of prospectivenodal network being subject to examination for nodal network handoff.Predictive model 4016 can return acceptance data as noted on a scale of0.0 to 1.0 so that higher values indicate a high likelihood ofacceptance and lower values indicate a lower likelihood of acceptance.Based on training data applied to predictive model 4016, predictivemodel 4016 is able to accurately predict a likelihood of a nodeaccepting an invite to thereby reconfigure a configuration of a nodalnetwork.

Various available tools, libraries, and/or services can be utilized forimplementation of predictive model 4004, 4008, and 4012. For example, amachine learning service provided by IBM® WATSON® can provide access tolibraries of APACHE® SPARK® and IBM® SPSS® (IBM® WATSON® and SPSS® areregistered trademarks of International Business Machines Corporation andAPACHE® and SPARK® are registered trademarks of the Apache SoftwareFoundation. A machine learning service provided by IBM® WATSON® canprovide access set of REST APIs that can be called from any programminglanguage and that permit the integration of predictive analytics intoany application. Enabled REST APIs can provide e.g. retrieval ofmetadata for a given predictive model, deployment of models andmanagement of deployed models, online deployment, scoring, batchdeployment, stream deployment, monitoring and retraining deployedmodels.

Certain embodiments herein may offer various technical computingadvantages involving computing advantages to address problems arising inthe realm of computer networks. Machine learning processes can beperformed for increased accuracy and for reduction of reliance on rulesbased criteria and thus reduced computational overhead. For enhancementof computational accuracies, embodiments can feature computationalplatforms existing only in the realm of computer networks such asartificial intelligence (AI) platforms, and machine learning platforms.Embodiments herein can employ data structuring processes, e.g.processing for transforming unstructured data into a form optimized forcomputerized processing. Embodiments herein can include particulararrangements for both collecting rich data into a data repository andadditional particular arrangements for updating such data and for use ofthat data to drive artificial intelligence decision making. Embodimentsherein can provide for intelligent handoff of access to a blockchainledger from a first candidate node to a second candidate node. Applyingartificial intelligence, handoff can be performed to that a succeedingengaged nodal network is well suited for performance of a missionreferenced in a blockchain ledger. Features can include predicting atopic associated to a ledger at a specific point in time and selecting asucceeding nodal network for engagement to the blockchain ledger independence on a matching between a topic of the nodal network and areturned topic of the blockchain ledger. Certain embodiments of thepresent invention may be implemented by use of a cloud platform/datacenter in various types including a Software-as-a-Service (SaaS),Platform-as-a-Service (PaaS), Database-as-a-Service (DBaaS), andcombinations thereof based on types of subscription. The staticoptimization service may be provided for subscribed business entitiesand/or individuals in need from any location in the world.

FIGS. 11-13 depict various aspects of computing, including a computersystem and cloud computing, in accordance with one or more aspects setforth herein.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as Follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as Follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 11, a schematic of an example of a computing nodeis shown. Computing node 10 is only one example of a computing nodesuitable for use as a cloud computing node and is not intended tosuggest any limitation as to the scope of use or functionality ofembodiments of the invention described herein. Regardless, computingnode 10 is capable of being implemented and/or performing any of thefunctionality set forth hereinabove. Computing node 10 can beimplemented as a cloud computing node in a cloud computing environment,or can be implemented as a computing node in a computing environmentother than a cloud computing environment.

In computing node 10 there is a computer system 12, which is operationalwith numerous other general purpose or special purpose computing systemenvironments or configurations. Examples of well-known computingsystems, environments, and/or configurations that may be suitable foruse with computer system 12 include, but are not limited to, personalcomputer systems, server computer systems, thin clients, thick clients,hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputer systems, mainframe computersystems, and distributed cloud computing environments that include anyof the above systems or devices, and the like.

Computer system 12 may be described in the general context of computersystem-executable instructions, such as program processes, beingexecuted by a computer system. Generally, program processes may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program processes may belocated in both local and remote computer system storage media includingmemory storage devices.

As shown in FIG. 11, computer system 12 in computing node 10 is shown inthe form of a computing device. The components of computer system 12 mayinclude, but are not limited to, one or more processor 16, a systemmemory 28, and a bus 18 that couples various system components includingsystem memory 28 to processor 16. In one embodiment, computing node 10is a computing node of a non-cloud computing environment. In oneembodiment, computing node 10 is a computing node of a cloud computingenvironment as set forth herein in connection with FIGS. 12-13.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system 12 typically includes a variety of computer systemreadable media. Such media may be any available media that is accessibleby computer system 12, and it includes both volatile and non-volatilemedia, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program processes that are configured to carry out thefunctions of embodiments of the invention.

One or more program 40, having a set (at least one) of program processes42, may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram processes, and program data. One or more program 40 includingprogram processes 42 can generally carry out the functions set forthherein. One or more program 40 including program processes 42 can definemachine logic to carry out the functions set forth herein. In oneembodiment, authority 110 can include one or more computing node 10 andcan include one or more program 40 for performing functions describedwith reference to method 200 of FIG. 2 and functions described withreference to authority 110 as set forth in the flowchart of FIG. 3. Inone embodiment, one or more candidate nodes N1-NZ can include one ormore computing node 10 and can include one or more program 40 forperforming functions described with reference to one or more candidatenode N1-NZ as set forth in the flowchart of FIG. 3. In one embodiment,the computing node based systems and devices depicted in FIG. 1 caninclude one or more program 40 for performing functions described withreference to such computing node based systems and devices.

Computer system 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computer system12; and/or any devices (e.g., network card, modem, etc.) that enablecomputer system 12 to communicate with one or more other computingdevices. Such communication can occur via Input/Output (I/O) interfaces22. Still yet, computer system 12 can communicate with one or morenetworks such as a local area network (LAN), a general wide area network(WAN), and/or a public network (e.g., the Internet) via network adapter20. As depicted, network adapter 20 communicates with the othercomponents of computer system 12 via bus 18. It should be understoodthat although not shown, other hardware and/or software components couldbe used in conjunction with computer system 12. Examples, include, butare not limited to: microcode, device drivers, redundant processingunits, external disk drive arrays, RAID systems, tape drives, and dataarchival storage systems, etc. In addition to or in place of havingexternal devices 14 and display 24, which can be configured to provideuser interface functionality, computing node 10 in one embodiment caninclude display 25 connected to bus 18. In one embodiment, display 25can be configured as a touch screen display and can be configured toprovide user interface functionality, e.g. can facilitate virtualkeyboard functionality and input of total data. Computer system 12 inone embodiment can also include one or more sensor device 27 connectedto bus 18. One or more sensor device 27 can alternatively be connectedthrough I/O interface(s) 22. One or more sensor device 27 can include aGlobal Positioning Sensor (GPS) device in one embodiment and can beconfigured to provide a location of computing node 10. In oneembodiment, one or more sensor device 27 can alternatively or inaddition include, e.g., one or more of a camera, a gyroscope, atemperature sensor, a humidity sensor, a pulse sensor, a blood pressure(bp) sensor or an audio input device. Computer system 12 can include oneor more network adapter 20. In FIG. 12 computing node 10 is described asbeing implemented in a cloud computing environment and accordingly isreferred to as a cloud computing node in the context of FIG. 12.

Referring now to FIG. 12, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 12 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 13, a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 12) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 13 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and processing components 96 for intelligentledger access transfer as set forth herein. The processing components 96can be implemented with use of one or more program 40 described in FIG.11.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowcharts and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a,” “an,” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprise” (and any form ofcomprise, such as “comprises” and “comprising”), “have” (and any form ofhave, such as “has” and “having”), “include” (and any form of include,such as “includes” and “including”), and “contain” (and any form ofcontain, such as “contains” and “containing”) are open-ended linkingverbs. As a result, a method or device that “comprises,” “has,”“includes,” or “contains” one or more steps or elements possesses thoseone or more steps or elements, but is not limited to possessing onlythose one or more steps or elements. Likewise, a step of a method or anelement of a device that “comprises,” “has,” “includes,” or “contains”one or more features possesses those one or more features, but is notlimited to possessing only those one or more features. Forms of the term“based on” herein encompass relationships where an element is partiallybased on as well as relationships where an element is entirely based on.Methods, products and systems described as having a certain number ofelements can be practiced with less than or greater than the certainnumber of elements. Furthermore, a device or structure that isconfigured in a certain way is configured in at least that way, but mayalso be configured in ways that are not listed.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below, if any, areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description set forth herein has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of one or more aspects set forth herein and the practicalapplication, and to enable others of ordinary skill in the art tounderstand one or more aspects as described herein for variousembodiments with various modifications as are suited to the particularuse contemplated.

What is claimed is:
 1. A computer program product comprising: a computerreadable storage medium readable by one or more processing circuit andstoring instructions for execution by one or more processor forperforming a method comprising: examining ledger data of a blockchainledger; examining node data of a plurality of candidate nodes, whereinthe examining node data includes examining data of candidate nodalnetworks associated to respective ones of the plurality of candidatenodes; and transitioning blockchain ledger access in dependence on theexamining of the ledger data and in dependence on the examining of thenode data, wherein the transitioning blockchain ledger access includestransitioning blockchain ledger access between a first candidate nodeand a second candidate node of the plurality of candidate nodes, whereinthe ledger data of the blockchain ledger includes data specifyingactions of a work project, wherein the examining ledger data includesperforming natural language processing of text based data of the ledgerdata to return a skill topic classification of the ledger data, whereinthe method includes returning a predicted performance of respectivenodal networks of candidate nodes of the plurality of candidate nodes,the returning including using a scoring function, wherein a returnedscore returned using the scoring function is in dependence on a matchingbetween skill topic classification of edges of the respective nodalnetworks, and a skill topic classification of the ledger data, andwherein the examining node data of the plurality of candidate nodesincludes examining social network data of the plurality of candidatenodes.
 2. The computer program product of claim 1, wherein candidatenodal networks associated to respective ones of the plurality ofcandidate nodes are social networks, and wherein the candidate nodalnetworks associated to respective ones of the plurality of candidatenodes include existing nodal networks, and prospective nodal networksthat can be prospectively generated, wherein the method includesproviding one or more output to generate an established nodal network inaccordance a prospective nodal network of the prospective nodalnetworks, and wherein the providing one or more output to generate theestablished nodal network is performed in dependence on the examining ofthe ledger data and in dependence on the examining of the node data, andwherein the providing one or more output to generate the establishednodal network includes providing one or more output to initiate aninvitation by a certain node of an existing nodal network to anothernode to socially connect to the certain node of the existing nodalnetwork.
 3. The computer program product of claim 1, wherein theplurality candidate nodes include candidate nodes of a blockchainnetwork that includes the blockchain ledger, wherein the transitioningincludes providing one or more output to relegate access of a first nodeof the plurality of candidate nodes to the blockchain ledger, andproviding one or more output to promote access of a second one of theplurality of candidate nodes to the blockchain ledger.
 4. The computerprogram product of claim 1, wherein the candidate nodal networksassociated to respective ones of the plurality of candidate nodesinclude existing nodal networks, and prospective nodal networks that canbe prospectively generated, wherein the method includes providing one ormore output to generate an established nodal network in accordance aprospective nodal network of the prospective nodal networks independence on the examining of the ledger data and in dependence on theexamining of the node data.
 5. The computer program product of claim 1,wherein the ledger data specifies transaction of a collaborative workproject being performed, and wherein the method includes predicting,based on the examining ledger data, a skill topic classification of thecollaborative work project at a future point in time occurring after aperceivable delay with respect to a current time, and wherein thetransitioning is performed in dependence on the predicting.
 6. Thecomputer program product of claim 1, wherein the candidate nodalnetworks associated to respective ones of the plurality of candidatenodes define social networks, and wherein the candidate nodal networksassociated to respective ones of the plurality of candidate nodesinclude existing nodal networks, and prospective nodal networks that canbe prospectively generated, wherein the method includes providing one ormore output to generate an established nodal network in accordance aprospective nodal network of the prospective nodal networks, and whereinthe providing one or more output to generate the established nodalnetwork is performed in dependence on the examining of the ledger dataand in dependence on the examining of the node data, and wherein theproviding one or more output to generate the established nodal networkincludes providing one or more output to initiate an invitation by acertain node of an existing nodal network to another node to sociallyconnect to the certain node of the existing nodal network.
 7. Thecomputer program product of claim 1, wherein the plurality of candidatenodes are member nodes of a blockchain network having access to theblockchain ledger, wherein the ledger data specifies transactions of acollaborative work project being performed, wherein the method includesproviding one or more output to reconfigure a nodal network associatedto the second candidate node of the plurality of candidate nodes independence on the examining of the ledger data and in dependence on theexamining of the node data, and wherein the method is performed so thatthere is engaged for performance of a collaborative work projectreferenced in the blockchain ledger an established nodal network that isestablished by the providing one or more output to reconfigure the nodalnetwork associated to the second candidate node.
 8. The computer programproduct of claim 1, wherein the candidate nodal networks associated torespective ones of the plurality of candidate nodes include existingnodal networks, and prospective nodal networks that can be prospectivelygenerated.
 9. A system comprising: a memory; at least one processor incommunication with the memory; and program instructions executable byone or more processor via the memory to perform a method comprising:examining ledger data of a blockchain ledger; examining node data of aplurality of candidate nodes, wherein the examining node data includesexamining data of candidate nodal networks associated to respective onesof the plurality of candidate nodes; and transitioning blockchain ledgeraccess in dependence on the examining of the ledger data and independence on the examining of the node data, wherein the transitioningblockchain ledger access includes transitioning blockchain ledger accessbetween a first candidate node and a second candidate node of theplurality of candidate nodes, wherein the ledger data of the blockchainledger includes data specifying actions of a work project, wherein theexamining ledger data includes performing natural language processing oftext based data of the ledger data to return a skill topicclassification of the ledger data, wherein the method includes returninga predicted performance of respective nodal networks of candidate nodesof the plurality of candidate nodes, the returning including using ascoring function, wherein a returned score returned using the scoringfunction is in dependence on a matching between skill topicclassification of edges of the respective nodal networks, and a skilltopic classification of the ledger data, and wherein the examining nodedata of the plurality of candidate nodes includes examining socialnetwork data of the plurality of candidate nodes.
 10. The system ofclaim 9, wherein examining ledger data includes examining ledger data toreturn a predicted topic classification of the ledger data at a futuretime perceivably delayed by a delay time relative to a current time. 11.The system of claim 9, wherein the ledger data specifies transaction ofa collaborative work project being performed, and wherein the methodincludes predicting, based on the examining ledger data, a skill topicclassification of the collaborative work project at a future point intime occurring after a perceivable delay with respect to a current time,and wherein the transitioning is performed in dependence on thepredicting.
 12. The system of claim 9, wherein the ledger data specifiestransactions of a collaborative work project being performed, whereinthe method includes providing one or more output to reconfigure a nodalnetwork associated to the second candidate node of the plurality ofcandidate nodes in dependence on the examining of the ledger data and independence on the examining of the node data, and wherein the method isperformed so that there is engaged for performance of a collaborativework project referenced in the blockchain ledger an established nodalnetwork that is established by the providing one or more output toreconfigure the nodal network associated to the second candidate node.13. The system of claim 9, wherein the candidate nodal networksassociated to respective ones of the plurality of candidate nodesinclude existing nodal networks, and prospective nodal networks that canbe prospectively generated, wherein the method includes providing one ormore output to generate an established nodal network in accordance aprospective nodal network of the prospective nodal networks independence on the examining of the ledger data and in dependence on theexamining of the node data.
 14. The system of claim 9, wherein examiningledger data includes examining data that specifies transactions of awork project being performed, wherein candidate nodal networksassociated to respective ones of the plurality of candidate nodes arerespective social networks of the respective candidate nodes, whereinthe transitioning blockchain ledger access includes providing one ormore output to relegate access of a first node of the plurality ofcandidate nodes and providing one or more output to promote access of asecond node of the plurality of candidate nodes.
 15. The system of claim9, wherein the candidate nodal networks associated to respective ones ofthe plurality of candidate nodes define social networks, and wherein thecandidate nodal networks associated to respective ones of the pluralityof candidate nodes include existing nodal networks, and prospectivenodal networks that can be prospectively generated, wherein the methodincludes providing one or more output to generate an established nodalnetwork in accordance a prospective nodal network of the prospectivenodal networks, and wherein the providing one or more output to generatethe established nodal network is performed in dependence on theexamining of the ledger data and in dependence on the examining of thenode data, and wherein the providing one or more output to generate theestablished nodal network includes providing one or more output toinitiate an invitation by a certain node of an existing nodal network toanother node to socially connect to the certain node of the existingnodal network.
 16. The system of claim 9, wherein the examining nodedata of the plurality of candidate nodes includes examining socialnetwork data of the plurality of candidate nodes.
 17. The system ofclaim 9, wherein the examining node data of the plurality of candidatenodes includes examining social network data of the plurality ofcandidate nodes, wherein the examining social network data includesexamining skill topic classifications of a plurality of edges of thecandidate nodal networks associated to respective ones of the candidatenodes, wherein the candidate nodal networks define social networksassociated to respective ones of the plurality of candidate nodes. 18.The system of claim 9, wherein the examining node data of a plurality ofcandidate nodes includes examining social network data of the pluralityof candidate nodes, the examining social network data includingexamining calendar availability data social network contacts of theplurality of candidate nodes.
 19. The system of claim 9, wherein theplurality candidate nodes include candidate nodes of a blockchainnetwork that includes the blockchain ledger, wherein the transitioningincludes providing one or more output to relegate access of a first nodeof the plurality of candidate nodes to the blockchain ledger, andproviding one or more output to promote access of a second one of theplurality of candidate nodes to the blockchain ledger.
 20. The system ofclaim 9, wherein the candidate nodal networks associated to respectiveones of the plurality of candidate nodes include existing nodalnetworks, and prospective nodal networks that can be prospectivelygenerated.